loom.executor.executor

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Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

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class loom.executor.executor.Executor(**data)

Bases: BaseModel

Factory class for executing quantum circuits using different target languages.

Parameters:

target_language (TargetLanguage) – The target language/platform for circuit execution

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

property converter: Converter

Return the converter for the target language.

export_eka(input_data)

Factory function that selects the right converter and runs it.

Parameters:

input_data (InterpretationStep) – The EKA interpretation step to convert

Returns:

The converted output in the target language format

Return type:

Any

export_eka_circuit(input_data)

Export the EKA circuit to the target language format.

Parameters:

input_data (Circuit) – The EKA circuit to convert

Returns:

The converted output in the target language format

Return type:

Any

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'target_language': FieldInfo(annotation=TargetLanguage, required=True, description='Target language for circuit conversion and execution')}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

parse_outcome(run_output)

Function to parse the measurement outcomes from the target language run output.

Parameters:

run_output (Any) – The output from running the converted circuit

Returns:

Parsed measurement outcomes as a dictionary mapping channels labels to outcomes or lists of outcomes (for multiple shots)

Return type:

dict[str, int | list[int]]

target_language: TargetLanguage

class loom.executor.executor.TargetLanguage(*values)

Bases: Enum

Enumeration of the available converters.

CUDAQ(**data) = <class 'loom.executor.eka_to_cudaq_converter.EkaToCudaqConverter'>

MIMIQ(**data) = <class 'loom.executor.eka_to_mimiq_converter.EkaToMimiqConverter'>

OPEN_QASM3(**data) = <class 'loom.executor.eka_to_qasm_converter.EkaToQasmConverter'>

PENNYLANE = functools.partial(<class 'loom.executor.eka_to_pennylane_converter.EkaToPennylaneConverter'>, is_catalyst=False)

PENNYLANE_CATALYST = functools.partial(<class 'loom.executor.eka_to_pennylane_converter.EkaToPennylaneConverter'>, is_catalyst=True)

STIM(**data) = <class 'loom.executor.eka_to_stim_converter.EkaToStimConverter'>